text: # Copyright 2019 The Forte Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import argparse
  
  import yaml
  
  from fortex.elastic import ElasticSearchQueryCreator, ElasticSearchProcessor
  
  from ms_marco_evaluator import MSMarcoEvaluator
  from reader import EvalReader
  from forte.common.configuration import Config
  from forte.data.multi_pack import MultiPack
  from forte.pipeline import Pipeline
  from forte.processors.ir.bert import BertRerankingProcessor
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
  
      parser.add_argument(
          "--config_file", default="./config.yml", help="Config YAML filepath"
      )
      args = parser.parse_args()
  
      # loading config
      config = yaml.safe_load(open(args.config_file, "r"))
      config = Config(config, default_hparams=None)
  
      # reading query input file
      parser.add_argument(
          "--input_file",
          default="./data/collectionandqueries/query_doc_id.tsv",
          help="Input query filepath",
      )
  
      input_file = config.evaluator.input_file
  
      # initializing pipeline with processors
      nlp: Pipeline = Pipeline[MultiPack]()
      eval_reader = EvalReader()
      nlp.set_reader(reader=eval_reader, config=config.reader)
      nlp.add(ElasticSearchQueryCreator(), config=config.query_creator)
      nlp.add(ElasticSearchProcessor(), config=config.indexer)
      nlp.add(BertRerankingProcessor(), config=config.reranker)
      nlp.add(MSMarcoEvaluator(), config=config.evaluator)
      nlp.initialize()
  
      for idx, m_pack in enumerate(nlp.process_dataset(input_file)):
          if (idx + 1) % 1000 == 0:
              print(f"Processed {idx + 1} examples")
  
      scores = nlp.evaluate()
      print(scores)
  